Statistical Disclosure Limitation in the Presence of Edit Rules

Kim Hang J. (), Karr Alan F. () and Reiter Jerome P. ()
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Kim Hang J.: Duke University and National Institute of Statistical Sciences, P.O. Box 90251, Durham, NC 27708, U.S.A
Karr Alan F.: RTI International, 3040 East Cornwallis Road, P.O. Box 12194, Research Triangle Park, NC 27709, U.S.A
Reiter Jerome P.: Department of Statistical Science, Duke University, P.O. Box 90251, Durham, NC 27708, U.S.A

Journal of Official Statistics, 2015, vol. 31, issue 1, 121-138

Abstract: We compare two general strategies for performing statistical disclosure limitation (SDL) for continuous microdata subject to edit rules. In the first, existing SDL methods are applied, and any constraint-violating values they produce are replaced using a constraint-preserving imputation procedure. In the second, the SDL methods are modified to prevent them from generating violations. We present a simulation study, based on data from the Colombian Annual Manufacturing Survey, that evaluates the performance of the two strategies as applied to several SDL methods. The results suggest that differences in risk-utility profiles across SDL methods dwarf differences between the two general strategies. Among the SDL strategies, variants of microaggregation and partially synthetic data offer the most attractive risk-utility profiles.

Keywords: Confidentiality; imputation; survey; synthetic data (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:31:y:2015:i:1:p:121-138:n:6

DOI: 10.1515/jos-2015-0006

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